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Free Software, More Voices, Better Plans

How future-ready, zero-cost grid models are democratizing grid planning.

A new breed of free, open-source grid models has quietly emerged that can compete with, or even beat, leading proprietary models in terms of the capabilities needed to plan for a rapidly evolving future grid. These digital tools are creating new opportunities for a diverse range of stakeholders to make their voices heard in the grid planning process.

As awareness has grown about the massive transformation needed to achieve local, state, and federal climate commitments, participation in grid planning processes has increased as well. In Kentucky, we see an example of how substantially, and quickly, this type of participation has grown. When Louisville Gas and Electric and Kentucky Utilities (LG&E-KU) submitted their 2018 integrated resource plan (IRP) to the Kentucky Public Service Commission, only two stakeholders formally participated in the process of reviewing and commenting on the IRP, in which utilities lay out their plans to meet forecasted demand through various resource options (e.g., energy efficiency, renewables, natural gas). Just three years later, for the utilities’ 2021 IRP, that number had risen to nine participating groups including citizen, municipal, environmental, and industry organizations. This rise in stakeholder participation has the potential to drive greater utility accountability and more equitable planning outcomes. But to unlock this potential, stakeholders need the right tools for the job.

Grid Models Are Evolving and Have Never Been More Accessible

Modeling is a key part of the grid planning process. A common type of grid model, called a capacity expansion model (CEM), helps grid planners make decisions about investments and retirements of power plants and other assets, usually over the next 10 to 15 years, by solving a complex optimization problem. The CEM aims to match available power from fossil and clean energy sources to forecasted energy demand to determine a “least cost” set of decisions.

Stakeholders participating in a grid planning process often seek to hold utilities accountable in their grid modeling by ensuring that they use the most up-to-date or accurate assumptions about resources and their capabilities, and that they best utilize a model’s features and functionalities. When stakeholders perform their own capacity expansion modeling, they can drive better planning outcomes by demonstrating the impact of changes to the model. However, these models can be prohibitively expensive, with access costing tens of thousands of dollars for a single user in some cases. The ability to perform grid modeling as part of the resource planning process has therefore mostly been limited to those who can afford it — major utilities. This limits the ability of nonutility stakeholders, often operating under tight budgets, to challenge a utility’s conclusions or bring their own modeling insights to the table.

Thanks to a passionate community of energy enthusiasts, academics, and software developers, however, it’s no longer necessary to spend a fortune on advanced grid models to contribute robust insights to the grid planning process. For example, the free, open-source GenX capacity expansion model, developed by researchers at MIT and Princeton, was recently used by environmental stakeholders in Florida to model cost effective, low-carbon planning pathways to inform JEA’s forthcoming IRP.

Uncovering Cleaner Planning Pathways in Kentucky with Zero-Cost Grid Models

In RMI’s new brief Power Planning to the People, we used GenX, along with data gathered from public data sources, to demonstrate how stakeholders might apply these models to identify better planning outcomes. Using LG&E-KU’s 2021 IRP as a case study, we developed a model that addressed key concerns that had been raised by stakeholders about the utilities’ approach. In doing so, we identified opportunities for customer savings through immediate development of new solar resources, accelerated retirement of existing coal plants, and portfolios with substantially less gas-fueled generation than had been proposed by the utilities.

While the utilities only evaluated a single set of technology cost forecasts, we evaluated two different cost trajectories for renewables. This provided insights into how lower costs for solar, storage, and wind impact the model’s selection of resources, and suggested that wind might become a more attractive option for the utilities (see Exhibit 1 below). As another example, while the utilities only used their CEM to optimize investments for a single year (2035), we modeled least-cost investments for every year in the utilities’ planning horizon, spanning 2022–2036 (see Exhibit 2). This increased granularity revealed that clean energy additions can reliably meet forecasted demand without the need for new gas until at least 2032, even though the utilities’ long-term plan deploys new gas in 2028.

Exhibit 1: Cumulative Nameplate Capacity Additions by 2036Exhibit 1 Cumulative Nameplate Capacity Additions by 2036
Note: The LG&E-LU IRP does not specify a nameplate capacity value for proposed gas combustion turbine (CT) generation, but the IRP denoted summer and winter capacities of 220 and 248 MW, respectively. We assume a 250 MW/unit nameplate capacity for gas-CTs. Sources: IRP scenario: LG&E-KU Integrated Resource Plan, Volume 1, Table 5-19; RMI scenarios: RMI analysis
Exhibit 2: Annual Resource Portfolios — Reference Renewables Costs
Resource nameplate capacities (MW) under reference renewables costs for each year in the planning horizon.Exhibit 2: Annual Resource Portfolios — Reference Renewables Costs
Sources: Existing capacity: EIA-860 and LG&E-KU 2021 Integrated Resource Plan, Volume 1, Table 5-4; New capacity: RMI analysis

These results show how improved modeling approaches can reveal lower-cost portfolios and opportunities to move more quickly toward clean energy resources. Stakeholders increasingly bringing such insights to the planning process in Kentucky and around the country could help drive improvements in utility-led modeling. It could also provide support to commissions working to ensure that utilities consider a robust and representative set of planning pathways during the IRP process.

Regulators Can Help Further Unlock the Value of Stakeholder-Driven Modeling

Even as a growing number of zero-cost grid planning tools become available to democratize grid planning, unlocking the full value of stakeholder-driven modeling requires actions from public utilities commissions to ensure that the process is transparent and accessible:

  • They can establish data reporting and transparency standards for IRPs, such as those recently ordered by the Oregon Public Utility Commission, and require that utilities share all nonconfidential data sources in a machine-readable (e.g., CSV) format, so that stakeholders can more easily evaluate key inputs and assumptions and integrate them into their own modeling.
  • They can require that utility models be made available to certain stakeholders at the utility’s expense, giving those parties the ability to run scenarios using the utility’s own planning models and for their results to be considered by regulators in the planning process, an approach recently taken in New Mexico.
  • Finally, regulators can ensure that stakeholders have the opportunity to review and offer feedback on utility modeling assumptions early in the planning process.

Stakeholders are showing up like never before in grid planning proceedings in Kentucky and around the country. And in advanced, zero-cost grid models, they have a new set of tools to help them deliver valuable new perspectives and drive more democratic planning outcomes.